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The role of data analytics in facility management and maintenance operations

Historically, facility management has been a reactive process where metrics were based on response or resolution time. However, modern facilities are becoming increasingly complex, producing a vast amount of data. Big data analytics is beneficial in facility management, as it can provide an understanding of what is happening, how it is happening, and what will happen in facilities. 

What is data analytics, and why is it important in FM and maintenance?

Data analytics is the science of analysing and processing raw data to make meaningful and actionable insights, which can then be used to inform business decisions. This raw data needs to be collected, organised, and cleaned before it can be analysed.

Facility management software is designed to help managers and maintenance teams control the facility’s day-to-day manual operations. Day-to-day operations may include managing buildings, assets, and the staff responsible for operations and maintenance.   

Thus, data analysis is important in facility management software as it helps optimise facility performance by identifying more efficient business methods and reducing costs. Companies can use data analytics to help improve communications and transparency and minimise downtime.  

Types of data analytics


Descriptive analysis is the foundation of data insight. It is the simplest and most common form of data analysis used in businesses. This data analysis type uses past data to answer the question, “What happened?” Descriptive data is typically presented in the form of dashboards, such as KPI dashboards, sales lead overviews, and monthly revenue reports.


Diagnostic analysis is the next step, finding the answer to the question, “Why did it happen?” To understand why it happened, diagnostic analysis takes the insights found in the descriptive analysis step and dives deeper into finding the cause of those outcomes. This type of data analysis is helpful to organisations, as it creates more connections between data and identifies the patterns of behaviour.


Predictive analysis uses previous data to answer the question, “What is likely to happen next?” Using data summarised in the descriptive and diagnostic analyses helps logically predict event outcomes. Logical predictions rely on statistical modelling, which requires additional human resources and technology to forecast.

Businesses can use predictive analysis for sales forecasting, customer success teams, risk assessment, and customer segmentation to establish which leads are most likely to convert. It is essential to understand that forecasting is only an estimate, and the accuracy of this estimate relies on the quality of the data being analysed.


This is the final type and the frontier of data analysis. Prescriptive data analysis looks at what happened, why it happened, and what might happen to establish what will happen next. Prescriptive analysis uses state-of-the-art data practices and technology that requires a large organisational commitment from companies.

While this type of data analytics is the most sought-after, it is the most complex type of analysis, with only some organisations equipped to perform it.

Prescriptive analysis involves algorithms, computational modelling techniques, statistical methods, and machine learning. All possible decision pathways or patterns are considered with their likely outcomes.

Data analytics techniques and tools

There are different methods and techniques that data analysts use to process data and extract relevant information. The most common analytical methods and techniques include:

  • Factor analysis
  • Regression analysis
  • Time series analysis
  • Cohort analysis
  • Monte Carlo simulations
  • Cluster analysis
  • Dispersion analysis
  • Discriminant analysis


Some examples of data analytic tools include Microsoft Excel, Tableau, SAS, and Power BI. While working with sensitive company data, businesses can opt for regular penetration testing. These tools check for vulnerabilities in the system, helping prevent data leaks and other cyberattacks.

What types of facility data should be tracked?

Two types of data may be analysed, qualitative and quantitative. Qualitative data are measures of value expressed as numbers, and quantitative data are measures of categorical variables.

There is an infinite sum of data points that businesses can consider tracking; however, here are five of the most commonly tracked data points:

  • Space occupancy levels
  • Work order response times
  • Planned vs. reactive maintenance
  • Cost per repair
  • Energy use and audits


It is also vital to track any data from workplace technology. Integrations allow companies to connect teams, facilities, and software and provide data insights to help increase efficiency.

Roles of data analytics in facility management software

Data analytics plays a significant role in facility management software, helping businesses with the following:

It creates sustainable facilities

In today’s world, every company is responsible for ensuring that facility operations are sustainable and green. Data analytics is an excellent way to improve efficiency and facility resource utilisation.

It can identify high energy consumption assets and decrease energy audit costs. This helps companies reduce their carbon footprint, which is rewarded in countries like the United Kingdom.

It improves asset management

A facility possesses a variety of assets, such as infrastructure and employee-related assets. Asset management is a significant task that requires precision, as inefficiency or inaccuracy can result in increased expenditure or detrimental outcomes.

With data analysis, facility managers can track the asset’s condition, utilisation, portfolio, and effectiveness. Infraspeak helps simplify building management with its own BSYS integration. It turns data into real actions by syncing data and setting up alerts when an asset is not working properly.

It improves visibility of the facility

Typically, facility managers calculate facility occupancy according to cost per head, calculating the cost of accommodating employees within a designated facility. Without adequate measurements and tools, calculations are inaccurate, and it is unknown if facilities are underutilised or functioning at optimal levels.

Data analytics is vital in providing facility managers visibility on facility occupancy, improving space utilisation, and reducing costs of wastage. Infraspeak offers full-scope economic analysis to provide a financial and operational overview of your inventory. This can also help address expansion more efficiently.

In a nutshell:

Data analytics plays a crucial role in facility management software by providing valuable insights and empowering businesses to optimise facility performance, improve operational excellence, enhance asset management, and reduce costs. 

By leveraging descriptive, diagnostic, predictive, and prescriptive data analysis techniques, facility managers can make informed decisions, streamline processes, and drive sustainable practices. 

It improves operational excellence

Data analytics provides real-time insights into how facilities utilise their assets. For instance, these insights can tell facility managers how and what resources are being used, if there is any wastage, how efficiently processes are running, and whether there are any challenges that employees are facing in optimising asset utilisation.

It reduces costs

The ability to identify problems early and fix them timeously is often less expensive than doing so at a later stage. This is true across the spectrum of processes and equipment. Data analytic tools can help predict repairs and allow for proactive maintenance across the facility.

With data analytics, facility managers can determine bottlenecks and money leaks in any process. This allows them to implement solutions that reduce wastage and costs. 

Reduce your costs by optimising your spending with Infraspeak. Its analytics reporting enables you to track and manage all costs incurred through dynamic insight into your spending.


Combining data analytics with facility management software ensures the facility is efficient and adaptive across operations. A Power BI integration, for example, allows you to cross-reference data such as operation information, work orders, and asset management information, providing the perfect integration to improve decision-making.


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